arXiv:2602.07078v1 Announce Type: new Abstract: Reinforcement Learning (RL) for Large Language Models (LLMs) often suffers from training collapse in long-horizon tasks due to exploding gradient variance. To mitigate this, a baseline is commonly introduced for advantage computation; however, traditional value models remain difficult to optimize, and standard group-based baselines overlook sequence heterogeneity. Although classic optimal baseline theory can achieve global variance reduction, it neglects token heterogeneity and requires prohibitive gradient-based computation. In this work, we derive the Optimal […]

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The ultimate deep-dive into memory systems, web search, skills, multi-agent architecture, and cloud deployment I’ve been following OpenClaw’s evolution for a while now. Every few weeks, there’s another update, another feature, another reason to reconsider how I work with AI agents. The beginner tutorials are everywhere now, but what about the stuff that actually makes you productive? The features that transform OpenClaw from a cool demo into a serious productivity engine? That’s exactly what this guide is about. […]

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arXiv:2604.14860v1 Announce Type: new Abstract: We study bandit best-arm identification with arbitrary and potentially adversarial rewards. A simple random uniform learner obtains the optimal rate of error in the adversarial scenario. However, this type of strategy is suboptimal when the rewards are sampled stochastically. Therefore, we ask: Can we design a learner that performs optimally in both the stochastic and adversarial problems while not being aware of the nature of the rewards? First, we show that designing such […]

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arXiv:2603.10057v1 Announce Type: new Abstract: Software supply-chain security requires provenance mechanisms that support reproducibility and vulnerability assessment under dynamic execution conditions. Conventional Software Bills of Materials (SBOMs) provide static dependency inventories but cannot capture runtime behaviour, environment drift, or exploitability context. This paper introduces agentic Artificial Intelligence Bills of Materials (AIBOMs), extending SBOMs into active provenance artefacts through autonomous, policy-constrained reasoning. We present an agentic AIBOM framework based on a multi-agent architecture comprising (i) a baseline environment reconstruction […]

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Database Functions Changed How I Write Backend Code (A Beginner’s Guide) TL;DR: Database functions let you move certain logic from your application code into the database itself. This guide explains what database functions are, why they matter for backend development, and how to write practical PostgreSQL functions you can start using immediately. What Is a Database Function? A database function is a named, reusable block of code that lives inside your database. You define it once and call […]

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arXiv:2602.17690v1 Announce Type: new Abstract: Graphic design generation demands a delicate balance between high visual fidelity and fine-grained structural editability. However, existing approaches typically bifurcate into either non-editable raster image synthesis or abstract layout generation devoid of visual content. Recent combinations of these two approaches attempt to bridge this gap but often suffer from rigid composition schemas and unresolvable visual dissonances (e.g., text-background conflicts) due to their inexpressive representation and open-loop nature. To address these challenges, we propose […]

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arXiv:2604.13739v1 Announce Type: cross Abstract: Thompson Sampling (TS) has attracted a lot of interest due to its good empirical performance, in particular in the computational advertising. Though successful, the tools for its performance analysis appeared only recently. In this paper, we describe and analyze SpectralTS algorithm for a bandit problem, where the payoffs of the choices are smooth given an underlying graph. In this setting, each choice is a node of a graph and the expected payoffs of […]

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arXiv:2603.17092v1 Announce Type: new Abstract: Sim-to-real transfer of locomotion policies often leads to performance degradation due to the inevitable sim-to-real gap. Naively fine-tuning these policies directly on hardware is problematic, as it poses risks of mechanical failure and suffers from high sample inefficiency. In this paper, we address the challenge of safely and efficiently fine-tuning reinforcement learning (RL) policies for dynamic locomotion tasks. Specifically, we focus on fine-tuning policies learned in simulation directly on hardware, while explicitly enforcing […]

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